Robot recommender system using affection-based episode ontology for personalization
Gi Hyun Lim, Seung Woo Hong, Inhee Lee, Il Hong Suh, Michael Beetz · 2013
This paper proposes a robot recommender system, which uses a hybrid filtering method based on n-gram affective event model. Nowadays there are strong tendency to utilize a robot for educational services, which can provide educational contents to enhances individual student's motivation. However, the current service robots can be more holistic systems to offer personalized robotic services to satisfy every individuals by reflecting their preferences. Here, robotic service can be another field to meet personal need. Hybrid approaches of personalization technology that combine collaborative filtering approaches and content-based approaches are proposed over the last decade. Especially, n-gram based approaches are proposed to utilize sequential information from very large data sets. This paper suggests an extends affective event model and its n-gram model combining fact semantic knowledge, event episodic knowledge and emotion. To show the validity of the proposed approach, we applied the scenario of English learning. The experiment results shows that an educational service robot recommend two students as different content types, even though they miss same question.